When talking about ‘Tesla Vision’ in a press call with journalists on Wednesday, Musk said:

This is a Tesla‑developed neural net. Although it’s somewhat hardware independent, it can be ‑‑ we can actually run this on Nvidia, AMD or Intel. We did pick the Nvidia titan GPU as the main chip for the neural net. But it was pretty tight call between particularly between AMD and Nvidia. But ultimately we felt that Nvidia had the better hardware.

In a blog post today, Nvidia corrected the information (see in full below). The company also announced that the DRIVE PX 2 is now in full production.

Both the Titan and the DRIVE PX 2 are using the Nvidia’s Pascal GPU architecture, which might explain the confusion.

The Volvo XC90 was previously going to become the first vehicle to use the platform with 100 units being deployed through the Swedish carmaker’s Drive Me autonomous-car pilot program next year, but Tesla beat them to market with the platform now going into each new Tesla.

Nvidia didn’t disclose the price of the Drive PX 2 platform, but it is believed to be an expensive piece of equipment. While unlikely to be representative of the price Tesla is paying for its production cars, Nvidia was selling the system to OEMs for their development programs at $15,000 per unit earlier this year.

It likely represents an important part of the cost of Tesla’s new hardware suite, which also includes 8 cameras, ultrasonic sensors, and a radar antenna. It’s impressive that Tesla is able to include the system in all vehicles as a standard hardware and enable its features for $5,000 or $8,000 depending on the options.

Tesla says that the new ‘supercomputer’ powered by the Drive PX 2 platform represents a 40x processing power increase over its previous system:

To make sense of all of this data, a new onboard computer with over 40 times the computing power of the previous generation runs the new Tesla-developed neural net for vision, sonar and radar processing software.

NVIDIA DRIVE PX 2 is an end-to-end AI computing system that uses groundbreaking approaches in deep learning to perceive and understand the car’s surroundings.

Our deep learning platform is open and lets carmakers first train their own deep neural networks on GPU supercomputers. Once loaded into the car, it processes the networks at high speed to provide the real-time, accurate response required for autonomous driving.